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In the current pandemic situation, patients with critical diseases are lacking immediate care which would reduce the mortality rate. This invention focuses on continuous monitoring of patient™s EEG signals for occurrence of any seizures in brain signals. This system is designed using machine learning algorithm for resource optimization thereby implemented using VLSI technology.</text>
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The system in the present invention keeps the private data locally in smartphones, shares trained parameters and builds a global consensus model. The feasibility and usability of the proposed system are evaluated by three experiments and related discussion. The experimental results show that the distributed deep learning system can reconstruct the behavior of centralized training. We also measure the cumulative network traffic in different scenarios and show that the partial parameter sharing strategy does not only preserve the performance of the trained model but also can reduce network traffic.</text>
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Mobile becomes an essential part of our life, and it is also an inevitable medium for communication in the present era. It keeps us connected from mail access, travel booking, etc. The amount of personal information and sensitive information like username, password, and transaction details are routinely stored in the device. There is a possibility of information access when the thief steals the Mobile. It is a massive data loss for the mobile owner, especially private information.</text>
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                <text>Web Usage Mining (WUM), is the process of mining user behaviour patterns from huge log fles. Weblogs provide substantial input to learning the identity of an online user. Analysis of these patterns extracted from the weblog datasets is currently being explored by various researchers. Due to the recent advent of automation, mining patterns from weblogs are automated. These automated mining processes focus on browsing habits and usage patterns. To make this process of gathering better, there are many ways to look at how users act and put them into relevant groups.Identifying, detecting, and classifying features that demarcate specifc traits that are related is an important task. Conventional research is designed to discover web usage mining strategies through clustering and classifcation methods. However, there is a need to focus on and improve the accuracy of the prediction systems that classify acquired features to fgure out the patterns of web users. Deep learning methods are used to mine weblog data to improve accuracy and precision. To improve user behaviour pattern mining, a two-level clustering process is introduced as Ensemble Fuzzy K-Means with Logit Boost Clustering (EFK-LBC) technique to extract the weblog. In this technique, a preprocessing step is included to remove redundant data and choose reliable log fles. The Fuzzy-K means clustering technique is used to identify behavioural patterns exhibited by recurrent users. Finally, the Logit Boost Clustering method is introduced to the data,that help in generating a strong cluster. Clustering of web users frequent behavioural patterns using the Logit Boost ensemble technique helps the proposed EFK-LBC method to improve newlinethe accuracy up to 88% and reduce the clustering time by 20% compared with existing approaches. Though the proposed EFK-LBC technique performs better for user identifcation, the different initialization of clusters provides various fnal clustering results.</text>
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                <text>Patent Number: 201941039882, Applicant: Dr. Debabrata Samanta.&#13;
The present disclosure presents a novel electronic booster control system for fully automotive driverless vehicle. The it discloses a system of vacuum booster with an automotive air compressor system which comprises a compression piston, a power transmission component and a power input part with integrated to plurality of sensors , servo controllers and a central control module of the fully automotive driverless vehicle.</text>
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                <text> Biometrically activated self defense device for women safety /</text>
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                <text>Patent Number: 20194103621, Applicant: Dr. Debabrata Samanta.&#13;
The present invention is related to biometrically activated self-defense device for women safety. The wearable personal self-defense and altering device, comprises a biometric unit, and portable source of electrical energy, an electric discharge unit and a processing unit configured to connect a mobile computing device.</text>
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                <text>Patent Number: 201941048191, Applicant: Dr. Debabrata Samanta.&#13;
The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well.</text>
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                <text>Patent Number: 201941045946, Applicant: Dr. Debabrata Samanta.&#13;
The present invention relates to method for face- recognition on the basis of sketch using deep convolution neural network. The objective of the present invention is to overcome the inadequacies of the prior art in techniques for face- recognition on the basis of sketches.</text>
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                <text>The remake is a phenomenon both well-known and immediately recognizable but in India it is not theoretically analyzed. However, by analyzing these remakes, we can understand how these films reflect some specific cultural differences between one state and other State in India. Here researcher has taken four original and its remake films to understand the phenomena of remake. The highly intensed watching the films has helped researcher to understand the difference of films original and remakes. Researcher took one Tamil Movie and its remake in Hindi and also a Malayalam Movie and its remake in Tamil. Films are Tamil Singam to Hindi Singam and Malayalam Manichithrathazhu to Tamil Chandramukhi.</text>
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